Most of our research are the following lines:

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque convallis mauris eget nisl bibendum elementum. Morbi et dui magna. Duis pretium turpis sed lectus consequat, in porttitor libero pulvinar. Proin consequat urna eget molestie finibus. Curabitur pretium porta mi, at laoreet libero venenatis sit amet. Cras congue nisl ac lobortis congue. Vestibulum nulla velit, auctor sit amet rutrum eu, congue at nulla.

Spatial Data Infrastructures (SDI) are a new approach for the creation, distribution and use of geographic information, with an emphasis on interoperability. SDIs seek to go beyond the simple distribution of previously existing maps and cartographic data, to serve as data sources powered by standardized Web services. SDIs have the potential to become fundamental elements for understanding space, disseminating geographic data and information along with metadata on provenance, quality and semantics. The typical SDI user is someone who needs to combine and integrate data from various sources to generate new insights on a field of study or application. In this perspective, SDIs can have a central role in areas such as environmental management and urban planning.

The volume and variety of geographic data available on the Web for the common citizen are increasing rapidly. Since the onset of Web 2.0, there is much interest on tools that allow people to geographically locate and describe aspects of their daily life, and to share such knowledge with other people. Initial applications show that it is possible to mobilize the interest of large numbers of citizens for the creation, dissemination and maintenance of geographic information on socially relevant themes. This line of research focuses on the design and implementation of computational tools and techniques that allow groups of people to act as human sensors, voluntarily (or unconsciously) contributing information for the common good. The research agenda includes investigating user motivation, data quality, user feedback and spatial coverage of contributions. We also work on methods for active and passive crowdsourcing and crowdsensing, seeking the application of recommendation systems to enhance volunteered contributions.

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque convallis mauris eget nisl bibendum elementum. Morbi et dui magna. Duis pretium turpis sed lectus consequat, in porttitor libero pulvinar. Proin consequat urna eget molestie finibus. Curabitur pretium porta mi, at laoreet libero venenatis sit amet. Cras congue nisl ac lobortis congue. Vestibulum nulla velit, auctor sit amet rutrum eu, congue at nulla.

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque convallis mauris eget nisl bibendum elementum. Morbi et dui magna. Duis pretium turpis sed lectus consequat, in porttitor libero pulvinar. Proin consequat urna eget molestie finibus. Curabitur pretium porta mi, at laoreet libero venenatis sit amet. Cras congue nisl ac lobortis congue. Vestibulum nulla velit, auctor sit amet rutrum eu, congue at nulla.

The demand for geographic data in applications on the Web is increasing. One of the most important resources to support this increased interest is the ability to recognize references to places in Web documents. If documents can be correctly and efficiently linked to places mentioned directly or indirectly in them, it becomes possible to improve and innovate in directions such as geographic indexing and querying, finding relationships based on spatial proximity or containment, and detecting localized trends for events and phenomena mentioned in social media.

A large share of the information available on the Web is geographically specific. References to geographic locations appear in the form of place names, postal addresses, postcodes, historical dates, demonyms, ethnicity, typical food and others. Many queries include place names and other geographic terms. Therefore, there is demand for mechanisms to search for documents both thematically (for instance, using a set of keywords) and geographically, based on places mentioned or referenced by the text. Similar techniques and resources can also apply to streaming data, such as Twitter messages or RSS feeds, providing the opportunity to index content in near-real-time, based on references to places.

However, while finding references to places in Web documents, ambiguity and uncertainty occur. Places can share a name with other places (Paris, besides being the capital of France, refers to more than sixty places around the world. Places are named using common language words (Park, Hope and Independence are American cities) and proper names (Washington, Houston and San Francisco). Also, a place can be associated to many names, like New York, NYC or The Big Apple, and to names in various languages. Ambiguity makes the resolution of references to places intrinsically context-based. Although there are important work on place-based information integration and retrieval, areas such as disambiguation are still in their infancy.

References to places can be straightforward and unambiguous as geographic coordinates or not. Other sources of geographic location information can be structured (postal addresses) or unstructured (place descriptions in text). They can also be direct (place names) or indirect (references to cultural characteristics associated to places), explicit (news headers) or implicit (“9/11”). Humans are often able to recognize references to places based on such evidence, but this association does not come so easily to automated systems. Addressing this problem is one of the tasks for Geographic Information Retrieval (GIR) research.

GIR extends Information Retrieval with geographic locations and metadata, taking it beyond the use of keywords. GIR studies methods and techniques for the retrieval of information from unstructured or partially structured sources, including relevance ranking, based on queries that specify both theme and geographic scope.

The expression Urban Computing designates the process of acquiring, integrating and analyzing large volumes of heterogeneous data, generated by various sources in the urban space. These sources range from environmental sensors to official governmental data, and include the direct participation of citizens in crowdsourcing or volunteered information initiatives. Data and information managed in this process are directed to the understanding and solution of urban problems that are typical of large cities in Brazil and abroad, such as mobility, public health, air and sound pollution, water and energy consumption, and many others. There is a three-fold concern: on improving the urban environment for human (co)existence, on improving urban quality of living, and on improving the conditions for the operation, by governmental authorities and public utility companies, of the various systems that comprise the city. Our objectives in Urban Computing research is to establish a qualified cycle for collection, integration and use of geographic information to the benefit of society, fostering the evolution of the state-of-the-art in topics along this cycle, such as spatial data infrastructures. Research outcomes are applied to typical urban problems, with an emphasis on the use of geographic location as a factor for data integration and for communicating findings, as feedback to the society.